Full OEE and Lost‑Hours Analytics for Production Line

Introduction

The production line is a high-capacity manufacturing system where all process stages must run in sync to ensure stable output at the planned speed. Even small disturbances in availability, performance, or quality can quickly accumulate into hours of lost productive time. In many plants, however, the only visible performance indicator is a single OEE value reported at the end of a shift or day. Traditional MES reports or spreadsheet summaries provide aggregated numbers but do not explain where the time was actually lost — whether the line was waiting for material, blocked by full buffers, stopped due to technical issues, or paused for operator actions. This creates a gap between the complexity of the equipment and the simplicity of the KPIs used to manage it, making it difficult to identify real bottlenecks and to align priorities between production, maintenance, and management.

The purpose of this use case is to close that gap by converting raw PLC signals from the production line into a clear, second-by-second view of how the line is actually operating, while also quantifying all lost time within the planned production period. The solution builds a consistent OEE model that calculates Availability, Performance, and Quality for every shift, day, product, and module. In addition, downtime is broken down into meaningful categories such as No Material, Blocked, Changeover, Maintenance, Fault, and Operator Break.

For management, this provides a reliable single source of truth to understand where efficiency is gained or lost and to support investment decisions with real data. For maintenance teams, it separates technical issues from organizational or material-related stops, helping to plan preventive actions more effectively. For operators and shift supervisors, the dashboards become a daily tool that shows how their decisions affect OEE in real time and highlights practical opportunities to increase output without installing new equipment.

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Data and Virtual State Model

The use case builds on data that already exists in the customer’s automation and production systems and organises it into a consistent virtual view of how the production line behaves over time. At the base layer are detailed PLC signals from all main modules of the line, including drives, transports, shears, buffers, stackers, infrastructure readiness, safety circuits, material presence, and operator commands; this is enriched with production orders, shift calendars, and quality information provided by MES or ERP systems. Together, these inputs describe not only whether the machine is electrically and pneumatically ready, but also whether material is present at each critical point, whether buffers are full, whether safety devices are active, and what product and speed targets apply in a given period.

To make this information usable for operations, the solution constructs a virtual state model that classifies every second of line behaviour into a small number of high‑level states: RUNNING, READY, FAULT, NO MATERIAL, and BLOCKED. RUNNING covers all moments when at least one of the main transports or shears is actively moving material; READY indicates that the line is technically ready (power, compressed air, safety all OK) but no drives are running; FAULT represents any critical fault, interlock, or safety stop; NO MATERIAL captures periods when the line is READY but material sensors show no workpieces at the input for longer than a defined threshold; and BLOCKED describes situations where upstream sections are forced to wait because downstream storage or stack sensors indicate full buffers or “stack not in position”. These states provide a shared language that is easy to understand for operators, engineers, and managers.

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Under the hood, state transitions are driven by explicit rule logic that combines multiple signals, conditions, and timeouts to ensure that system states remain stable and meaningful rather than changing due to short sensor fluctuations.
The logic follows a defined set of rules:

• READY is activated only when all required conditions are satisfied, such as power available, air pressure in normal range, no active alarms, safety circuits closed, and no drives running.
• NO MATERIAL is set only when the relevant input sensors remain inactive for longer than the configured timeout.
• BLOCKED is triggered when downstream conditions, such as full buffers or missing position signals, remain active beyond their allowed time limit.

Every resulting state is logged continuously, typically with a one-second resolution.
This creates a detailed time series showing:

• how long the line stayed in each state,
• the exact order of state changes,
• how states align with shifts and production orders.

This continuous state history forms the foundation for all further analytics.
It is used to:

• define planned versus operating time,
• calculate OEE accurately,
• generate Pareto analysis of lost hours,
• build timeline visualisations,
• compare performance between shifts, orders, or periods.

The result is a transparent and consistent view of how the production line is actually used over time, enabling reliable performance analysis and data-driven decisions.

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OEE Calculation and Lost Hours Analytics

The analytics layer builds directly on the virtual state model to calculate OEE in a way that is both fully automated and easy to interpret in daily operations. Planned production time for each shift or day is derived from the production calendar, excluding scheduled breaks and planned maintenance, and defines the window in which OEE is evaluated. Within this window, operating time is defined as all periods when the line is RUNNING, plus optional micro‑stops that are short enough to be treated as part of normal operation rather than as separate downtime; the threshold for such micro‑stops can be tuned to each customer. Time spent in non‑productive states such as NO MATERIAL, BLOCKED, FAULT, MAINTENANCE, or longer operator‑related stops is excluded from operating time and instead contributes to “lost hours”, making the impact of each non‑running condition explicitly visible.

From this foundation, the system calculates the three OEE components for every relevant slice of the process: per shift, per day, per product, and per module of the production line. Availability expresses the ratio between operating time and planned production time and is directly linked to the proportion of time spent in productive vs non‑productive states. Performance compares actual throughput against the ideal throughput derived from nominal or target speed per product, using workpiece detection sensors and counters to measure how many sheets or joints were actually processed in each interval. Quality is determined from customer data on good versus scrap output per period, synchronised with the same timeline as states and throughput. Combining these three components yields OEE as a single KPI for each time slice, but the dashboards always allow users to drill down into the underlying components and see how they evolve over time.

Within planned production time, any second when the line is not producing good output is treated as a potential “lost hour” and is classified according to the state model, sensor patterns, operator actions, and calendar context. The analytics engine examines all non‑RUNNING intervals and assigns them to clearly defined loss categories, turning diffuse impressions of “downtime” into measurable buckets of waste. Typical categories include No Material, where the line is READY but upstream sensors show no workpieces; Blocked, where upstream sections are waiting because downstream buffers or stacks are full or not in position; Operator Break / No Operator, where the machine is READY and material is present but no start command is given outside scheduled breaks; Changeover / Setup, where the line is intentionally stopped or running in special modes during order changes and alignment; Maintenance and Adjustment, where protective fences are open or interlocks are active in line with maintenance actions; Fault / Safety Stops, where the line is in FAULT due to technical or safety issues; and an Other category for intervals that do not yet match any rule and can be refined over time.

On the OEE overview dashboard, these calculations come together in a compact view that shows OEE, Availability, Performance, and Quality across days and shifts, alongside a breakdown of operating versus lost time. Users can immediately see how today compares to last week, which shifts are closest to the target line, and whether changes in OEE are driven more by availability, performance, or quality. A dedicated Lost Hours & Pareto view then sorts loss categories by impact, using bars and cumulative curves to highlight where most productive time is being lost and how each category contributes to the overall gap between actual and potential output. This combination of high‑level KPIs with detailed loss analytics enables teams to move from abstract discussions about “low OEE” to very concrete, data‑backed improvement actions focused on the categories that matter most.

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‍Dashboards and Use Cases

The dashboards present the collected data in a form that can be directly used in daily operation and decision-making on the production line. A key element is the timeline view, which shows the full shift as a sequence of colour-coded machine states such as RUNNING, READY, FAULT, NO MATERIAL, and BLOCKED. This makes it easy to see when production was running and where time was lost. Orders and changeovers can be displayed on the same timeline, allowing state changes to be linked to specific events.

To compare performance, the system provides shift and operator benchmarking. OEE components and lost-time categories are aggregated for each shift or operator, making it possible to quickly see differences in availability, performance, and downtime reasons. This helps identify best practices as well as areas that require improvement or additional training.

The analytics can run as a standalone application or be integrated into existing systems. Connections to MES, ERP, or reporting tools allow production data, orders, and KPIs to stay consistent across the organization. Role-based access ensures that managers, engineers, and operators see only the level of detail they need, making the dashboards easy to use in everyday work.

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Business Value

From a business perspective, the dashboards turn OEE from a static KPI into a practical improvement tool. By showing exactly where time is lost and how this pattern changes over days and weeks, they help teams prioritise actions with the highest leverage, whether that is improving material logistics to reduce NO MATERIAL time, adjusting buffer and stack‑handling processes to cut BLOCKED losses, or standardising changeovers. Even relatively small gains in availability or performance translate directly into additional productive hours on the existing equipment, postponing or reducing the need for capital investments. At the same time, the quantified breakdown of losses provides a solid, data‑driven basis for justifying investments when they are needed, such as additional buffers, automation around stack handling, or extra staffing on specific shifts. Over time, this combination of transparency, feedback, and measurable impact supports a continuous improvement culture in which management, maintenance, and operators work from the same facts and can see the results of their decisions on the production line.

Conclusions and Next Steps

The implementation of full OEE and lost‑hours analytics on the production line has turned raw machine data into a shared, operational picture of how the line actually runs over time. The team now has transparent visibility of when the line is RUNNING, READY, starved of material, blocked by downstream equipment, or stopped due to faults, maintenance, or operator‑related reasons, all quantified down to seconds within planned production time. Instead of debating why OEE is low in general, managers, engineers, and operators can see a factual breakdown of losses by category, shift, product, and module, which has already enabled targeted actions such as reducing “No Material” time, smoothing stack changes, and tightening changeover procedures. As a result, the production line has gained measurable improvements in net productive time and stability without any additional investment in hardware, while also reducing manual reporting effort and ambiguity in performance discussions.